Automatic Circulating Tumor Cell Segmentation and Enumeration in Digital Pathology by Using Deep Learning and Ball-scale Based Filtering Techniques
Bibliographic record
Abstract
Circulating tumor cells (CTCs) shed from the primary tumor, intravasate into blood, and translocate to distant tissues via circulation [1]. CTC enumeration allows cancer detection, treatment monitoring, and survival prediction [2], [3]. In the clinical setting immunofluorescence-based CTC enumeration is primarily used by expert cytopathologists. Manual enumeration requires cytopathologists with rich experience to read hundreds to thousands of images in hours. Despite the seemingly high number, this poor efficiency hinders the relevant clinical implementation. Therefore, high-automation enumeration is missing but highly desired [4]. Here, we proposed an automatic CTC segmentation and enumeration method in digital pathology by using deep learning techniques. To prepare for enumeration, peripheral blood mononuclear cells (PBMC) were extracted from cancer patient blood followed by infection with a reengineered adenovirus, i.e., rAdCTC, which is a CD46-targeting, DF3 promoter-selective, and GFP-overexpression adenovirus. The rAdCTCensures detection specificity and efficiency of expression for CTCs. Subsequently, PBMCs were stained with anti-CD45 fluorescence-labeled antibody and DNA staining dye DAPI. Finally, the acquired fluorescence images were used for automatic segmentation and enumeration [5]. It is noteworthy that the fluorescence images used in this study contain three channels. The green, red, and blue signals respectively represent overexpressed GFP in infected cells, CD45 staining on leukocyte membranes, and nuclear staining.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".